BMC Emergency Medicine is calling for submissions to our Collection, Artificial intelligence in emergency medicine. The integration of artificial intelligence (AI) in emergency medicine is revolutionizing the way healthcare professionals respond to critical situations. AI technologies, including machine learning algorithms and predictive analytics, are being employed to enhance clinical decision support, optimize patient triage, and improve diagnostic imaging. These advancements are not only streamlining emergency response but also enabling healthcare providers to make more informed decisions in high-pressure environments. As the demand for efficient and effective emergency care continues to grow, the role of AI in this field is becoming increasingly vital.

Advancing our collective understanding of AI in emergency medicine is crucial for harnessing its full potential to improve patient outcomes and healthcare efficiency. Recent breakthroughs have demonstrated the effectiveness of AI algorithms in predicting patient deterioration, enhancing diagnostic accuracy, and facilitating timely interventions. Moreover, the integration of telemedicine and health informatics with AI tools has opened new avenues for remote patient monitoring and care delivery. Continued research in this area will help refine these technologies and ensure they are effectively implemented in clinical practice.

Looking ahead, the future of AI in emergency medicine holds immense promise. As research progresses, we may witness the development of more sophisticated AI systems capable of real-time data integration from various sources, leading to enhanced situational awareness for emergency responders. Additionally, advancements in natural language processing could facilitate better communication between AI systems and healthcare providers, ultimately improving decision-making processes in emergency settings.

AI algorithms for predictive analytics in emergency care

Clinical decision support systems in emergency medicine

Enhancements in diagnostic imaging through AI

Optimizing patient triage with machine learning

Telemedicine applications in emergency response

All manuscripts submitted to this journal, including those submitted to collections and special issues, are assessed in line with our editorial policies and the journal’s peer-review process. Reviewers and editors are required to declare competing interests and can be excluded from the peer review process if a competing interest exists.

BMC Emergency Medicine is calling for submissions to our Collection on prehospital management in out-of-hospital cardiac arrest (OHCA). OHCA remains a leading cause of mortality worldwide, and its management in the crucial moments before hospital arrival is critical to improving survival and good neurological outcomes. The first minutes following OHCA are crucial, requiring immediate action from bystanders, efficient call triage by EMDs, and rapid response by EMS teams.

Understanding the key factors influencing prehospital OHCA management —including public training, defibrillation access, recognition and telephone CPR assistance by EMDs, EMS deployment strategies, care provided and medical decision-making by EMS teams — is essential to improving patient outcomes. Recent research has demonstrated that early, high-quality interventions can significantly increase good neurological survival rates. Advances in bystander CPR programs, real-time assistance by dispatcher and community-wide defibrillator deployment have already shown significant benefits, but further research is needed to refine and standardize these approaches. Additionally, studies assessing EMS response optimization, prehospital triage (or video-assisted triage), and resource allocation during cardiac emergencies remain crucial to improving OHCA outcomes. New technologies, including mobile health applications, AI-assisted triage systems, and wearable devices, offer promising avenues for enhancing both bystander, emergency medical dispatch and EMS performance. Collaborative efforts among healthcare providers, researchers and policy makers are essential to establish standardised, evidence-based protocols to maximise the rate of good neurological outcomes following out-of-hospital cardiac arrest (OHCA).

We invite contributions that examine a broad range of topics related to the prehospital management of OHCA, including, but not limited to:

Community engagement and public training initiatives to improve OHCA response

Public access defibrillation and strategies to optimize early defibrillation

Effectiveness of bystander CPR and dispatcher-assisted CPR programs (including live video assistance)

Emergency medical dispatch (EMD) strategies for improved OHCA recognition and triage

EMS response time, efficiency and prehospital medical decision-making strategies (triage)

Specialized pre-hospital practices and advanced pre-hospital care (advanced airway management, ultrasonography, specific drugs, etc.)

Integration of mobile technology, AI, and real-time decision support for EMS responders

Legal and ethical challenges in prehospital OHCA care, including DNR orders, declaring death in the field, and the impact of regional differences on resuscitation decisions.

All manuscripts submitted to this journal, including those submitted to collections and special issues, are assessed in line with our editorial policies and the journal’s peer-review process. Reviewers and editors are required to declare competing interests and can be excluded from the peer review process if a competing interest exists.